Slope estimator for the linear error-in-variables model

Saqr, Anwar and Khan, Shahjahan ORCID: (2012) Slope estimator for the linear error-in-variables model. In: 12th Islamic Countries Conference on Statistical Sciences (ICCS 2012): Statistics for Everyone and Everywhere, 19-22 Dec 2012, Doha, Qatar.

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It is well known that in the presence of errors-in-variable the ordinary least squares (OLS) estimator of the parameters of the regression model is inappropriate. This is true even if the ratio of error variances is known. Wald's grouping method could deal with such problem but it lacks efficiency and is subject to identifiability problem. The main aim of the paper is to introduce a reflection based grouping method to improve the efficiency of the Wald's estimator under flexible assumption on the ratio of error variances. We compare the relative performance of the proposed reflection grouping (RG) estimator with the OLS, ML, Wald's and Geary's estimators by simulation studies under various conditions. The simulation results show that the RG estimator is more consistent and efficient than the other estimators.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 Islamic Countries Society of Statistical Sciences.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited: 29 Oct 2013 00:27
Last Modified: 27 Jun 2017 05:31
Uncontrolled Keywords: linear regression models; measurement error; reflection of points; ratio of error variances; method of cumulants; instrumental variable; method of moments
Fields of Research (2008): 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
01 Mathematical Sciences > 0101 Pure Mathematics > 010111 Real and Complex Functions (incl. Several Variables)
01 Mathematical Sciences > 0102 Applied Mathematics > 010203 Calculus of Variations, Systems Theory and Control Theory
Fields of Research (2020): 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490501 Applied statistics
49 MATHEMATICAL SCIENCES > 4904 Pure mathematics > 490411 Real and complex functions (incl. several variables)
49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490103 Calculus of variations, mathematical aspects of systems theory and control theory
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences

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